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77 results for “Local networks”
Spatial variation in early-winter snow cover determines local dynamics in a network of alpine butterfly populations
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Natural enemy-herbivore networks along local management and landscape gradients in urban agroecosystems
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Data from: Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks
Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs), interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities) that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity.
Data from: Network structure and local adaptation in coevolving bacteria-phage interactions
Numerous theoretical and experimental studies have investigated antagonistic coevolution between parasites and their hosts. Although experimental tests of theory from a range of biological systems are largely concordant regarding the influence of several driving processes, we know little as to how mechanisms acting at the smallest scales (individual molecular and phenotypic changes) may result in the emergence of structures at larger scales, such as coevolutionary dynamics and local adaptation. We capitalized on methods commonly employed in community ecology to quantify how the structure of community interaction matrices, so called 'bipartite networks', reflected observed coevolutionary dynamics, and how phages from these communities may or may not have adapted locally to their bacterial hosts. We found a consistent nested network structure for two phage types, one previously demonstrated to exhibit arms race coevolutionary dynamics and the other fluctuating coevolutionary dynamics. Both phages increased their host ranges through evolutionary time, but we found no evidence for a trade off with impact on bacteria. Finally, only bacteria from the arms race phage showed local adaptation, and we provide preliminary evidence that these bacteria underwent (sometimes different) molecular changes in the wzy gene associated with the LPS receptor, while bacteria coevolving with the fluctuating selection phage did not show local adaptation and had partial deletions of the pilF gene associated with Type IV pili. We conclude that the structure of phage-bacteria interaction networks is not necessarily specific to coevolutionary dynamics, and discuss hypotheses for why only one of the two phages was, nevertheless, locally adapted.
Data from: Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure
Recent advances in sequencing allow population-genomic data to be generated for virtually any species. However, approaches to analyse such data lag behind the ability to generate it, particularly in nonmodel species. Linkage disequilibrium (LD, the nonrandom association of alleles from different loci) is a highly sensitive indicator of many evolutionary phenomena including chromosomal inversions, local adaptation and geographical structure. Here, we present linkage disequilibrium network analysis (LDna), which accesses information on LD shared between multiple loci genomewide. In LD networks, vertices represent loci, and connections between vertices represent the LD between them. We analysed such networks in two test cases: a new restriction-site-associated DNA sequence (RAD-seq) data set for Anopheles baimaii, a Southeast Asian malaria vector; and a well-characterized single nucleotide polymorphism (SNP) data set from 21 three-spined stickleback individuals. In each case, we readily identified five distinct LD network clusters (single-outlier clusters, SOCs), each comprising many loci connected by high LD. In A. baimaii, further population-genetic analyses supported the inference that each SOC corresponds to a large inversion, consistent with previous cytological studies. For sticklebacks, we inferred that each SOC was associated with a distinct evolutionary phenomenon: two chromosomal inversions, local adaptation, population-demographic history and geographic structure. LDna is thus a useful exploratory tool, able to give a global overview of LD associated with diverse evolutionary phenomena and identify loci potentially involved. LDna does not require a linkage map or reference genome, so it is applicable to any population-genomic data set, making it especially valuable for nonmodel species.
Data for: Local contributions to beta-diversity in urban pond networks: implications for biodiversity conservation and management
<p><b>Aim:</b> An understanding of how biotic communities are spatially organised is necessary to identify and prioritize habitats within landscape-scale biodiversity conservation. Local Contribution to Beta diversity (LCBD) identifies individual habitats that make a significant contribution to beta-diversity and may have important practical implications, particularly for conservation of habitat networks. In this study, we develop and apply a conservation prioritisation approach based on LCBD in aquatic invertebrate communities from 132 ponds.</p> <p><b>Location:</b> Five urban settlements in the UK: Halton, Loughborough, Stockport, Birmingham, Huddersfield.</p> <p><b>Methods:</b> We partition LCBD into richness difference (nestedness: <sub>RichDiff</sub>LCBD) and species replacement (turnover: <sub>Repl</sub>LCBD) and identify key environmental variables driving LCBD. We examine LCBD at two scales relevant to conservation planning: within urban settlements and nationally across the UK.</p> <p><b>Results:</b> Significant differences in LCBD values were recorded among the five settlements. In four of the five urban settlements studied, pond sites with the greatest LCBD values typically showed high replacement values. Significant LCBD sites and sites with high taxonomic diversity together supported more of the regional species pool (70%-97%) than sites with high taxonomic diversity alone (54% to 94%) or what could be protected by the random selection of sites. LCBD was significantly associated with vegetation shading, surface area, altitude and macrophyte cover.</p> <p><b>Main conclusions:</b> Conservation prioritisation that incorporates LCBD and sites with high taxonomic diversity improves the effectiveness of conservation actions within pond habitat networks, ensures site supporting high biodiversity are protected, and provides a method to define a spatial network of protected sites. Identifying new, effective conservation approaches, particularly in urban areas where resources may be scarce and conflicts regarding land use exist, is essential to ensure biodiversity is fully supported and detrimental anthropogenic effects are reduced.</p>
Pollinator richness, pollination networks and diet adjustment along local and landscape gradients of resource diversity
<p>Loss of habitats and native species, introduction of invasive species, and changing climate regimes lead to the homogenization of landscapes and communities, affecting the availability of habitats and resources for economically important guilds, such as pollinators. Understanding how pollinators and their interactions vary along resource diversity gradients at different scales may help to determine their adaptability to current diversity loss related to global change. We used data on 20 plant-pollinator communities along gradients of flower richness (local diversity) and landscape heterogeneity (landscape diversity) to understand how the diversity of resources at local and landscape scales affected: (1) wild pollinator abundance and richness (accounting also for honeybee abundance); (2) the structure of plant-pollinator networks; (3) the proportion of actively selected interactions (those not occurring by neutral processes) and (4) pollinator diet breadth and species' specialization in networks. Wild pollinator abundance was higher overall in flower-rich and heterogeneous habitats, while wild pollinator richness increased with flower richness (more strongly for beetles and wild bees) and decreased with honeybee abundance. Network specialization (H2'), modularity, and functional complementarity were all positively related to floral richness and landscape heterogeneity, indicating niche segregation as the diversity of resources increases at both scales. Flower richness also increased the proportion of actively selected interactions (especially for wild bees and flies), whereas landscape heterogeneity had a weak negative effect on this variable. Overall, network-level metrics responded to larger landscape scales than pollinator-level metrics did. Higher floral richness resulted in a wider taxonomic and functional diet for all the study guilds, while functional diet increased mainly for beetles. Despite this, specialization in networks (d') increased with flower richness for all the study guilds, because pollinator species fed on a narrower subset of plants as communities became richer in species. Our study indicates that pollinators are able to adapt their diet to resource changes at local and landscape scales. However, resource homogenization might lead to poor and generalist pollinator communities, where functionally specialized interactions are lost. This study highlights the importance of including different scales to understand the effects of global change on pollination service through changes in resource diversity.</p>
Data from: Computing the local field potential (LFP) from integrate-and-fire network models
Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.
Data from: Local extinctions of obligate frugivores and patch size reduction disrupt the structure of seed dispersal networks
A central problem in ecology is to understand how human impacts affect plant-animal interactions that lead to effective seed dispersal services for plant communities. Seed dispersal services are the outcome of plant-frugivore interactions that often form local networks of interacting species. Recent work has shown that some frugivorous bird species are more critical to network organization than others. Here, we explore how patch size and the potential local extinctions of obligate frugivorous birds affect the reorganization of seed dispersal networks. We examined the structure of 20 empirical seed dispersal networks documented across tropical avian assemblages occupying widely variable habitat patch sizes, a surrogate of the amount of remaining habitat. Networks within small forest patches consistently supported both lower plant and bird species richness. Forest patch size was positively associated with nestedness, indicating that reductions in patch size disrupted the nested organization of seed dispersal networks. Obligate frugivores, especially large-bodied species, were almost entirely absent from small forest patches. Analysis at the species level showed that obligate frugivores formed the core of interacting species, connecting species within a given seed dispersal network. Our combined results revealed that patch size reduction erodes frugivorous bird diversity, thereby affecting the integrity of seed dispersal networks. We highlight the importance of conserving large forest patches to maintain tropical forest functionality.
Data from: Using local ecological knowledge to build mutualistic networks in hyper-diverse and logistically challenging ecosystems
<p>1. Collecting interaction data to build frugivory or seed dispersal networks is logistically challenging in ecosystems that have very high plant and animal diversity and/or where fieldwork is difficult or dangerous. Consequently, the majority of available networks are from ecosystems with low species diversity or they represent a sub-set of the community. </p> <p>2. Here, we propose an approach using local ecological knowledge (LEK) of indigenous communities to build interaction databases and weighted networks that would otherwise be difficult to achieve with direct observations. Indigenous communities live and work in many hyper-diverse ecosystems and the people within these communities often have detailed knowledge of ecological processes. </p> <p>3. Working in a Sundaland biodiversity hotspot – Royal Belum State Park, Peninsular Malaysia – we used field data, visually-oriented interviews with indigenous people (Orang Asli, in the Jahai and Temiar ethnic subgroups), and published records to collate interactions, and their frequency of occurrence of animal fruit consumption and seed dispersal. </p> <p>4. We documented 2060 fruit consumption and 1330 seed dispersal interactions among 164 plant species and 34 animal taxa, the latter representing groups of closely related species or individual species. The majority of the interactions (97%) were identified by the LEK interviews, with the additional methods (field data and published records) used to support and marginally expand the interview data. The metrics for the networks we built reflect those of networks structured by biological mechanisms, supporting the validity of our novel method. </p> <p>5. Local ecological knowledge is highly relevant for building detailed databases for mutualistic interactions in hyper-diverse and/or challenging ecosystems. Such ecosystems are among the most vulnerable on earth, harbouring ecological interactions that are often poorly documented at a community-level. We show how LEK can broaden our knowledge of such sensitive ecosystems, but our approach is useful for any ecosystem where people retain rich local ecological knowledge.</p>
Data from: Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure
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Data from: Network structure and local adaptation in coevolving bacteria-phage interactions
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Data from: Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks
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Data from: Computing the local field potential (LFP) from integrate-and-fire network models
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Data from: Using local ecological knowledge to build mutualistic networks in hyper-diverse and logistically challenging ecosystems
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Data from: Local extinctions of obligate frugivores and patch size reduction disrupt the structure of seed dispersal networks
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Data for: Local contributions to beta-diversity in urban pond networks: implications for biodiversity conservation and management
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Pollinator richness, pollination networks and diet adjustment along local and landscape gradients of resource diversity
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A Local Regulatory Network Around Three NAC Transcription Factors in Stress Responses and Senescence in Arabidopsis leaves (expression between lines at timepoints).
GEO Series GSE45183. Arabidopsis thaliana. 100 samples. Type: Expression profiling by array.
Spatial transcriptomic profiling reveals local and domain-establishing regulatory signaling networks within the bone marrow
GEO Series GSE228534. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.